In the international communication conference held at Geneva, Switzerland in 1993, C. Berrou, A. Glavieux and P. Thitimajshiwa from the British Communications University of France firstly proposed an encoding/decoding scheme referred to as a Turbo code. It is realized by combining two Recursive Systematic Convolutional (RSC) codes in a parallel cascading manner via an interleaver. This scheme adopts an iterative feedback decoding mode, truly explores a potential of cascaded codes, and breaks through a minimum distance short-code design idea in a random-like coding/decoding scheme, thereby making it more approach an ideal random code performance. Some existing communication systems such as a 3GPP LTE/LTE-A system, a WCDMA system and a TD-SCDMA system use Turbo codes for channel encoding.
The Turbo code has the characteristics of random-like codes, and also has sufficient structural information, which makes it possible to be decoded by using an efficient iterative decoding method. Due to these characteristics, the Turbo code has extremely beneficial performances under the conditions of a moderate bit error rate and a long packet length particularly. Actually, when any code rate and information packet length are greater than 104 bits, if a signal-to-noise ratio is within a Shannon limit 1 dB, the Turbo code using an iterative decoding algorithm can reach a bit error rate of 10-5, that is, an Eb/NO value under this code rate reaches a channel capacity.
The basic principle of Turbo decoding lies in iterative estimation of an information symbol based on an MAP algorithm, and inputting gradually updated prior information obtained by all pieces of statistically independent information after the previous decoding iteration. An iterative decoding concept is very similar to negative feedback, output external information is fed back to an input end to achieve an effect of amplifying the signal-to-noise ratio of the input end, thus stabilizing system output. By means of sufficient iterations, a final decoding decision may be obtained from a posterior probability Log Likelihood Ratio (LLR) value of any one decoder.
Most of the current MAP decoding schemes reduce the decoding delay and improve the throughput by using a decoding structure consisting of sub-blocks and sliding windows. In order to obtain more accurate boundary values, an existing method pre-calculates a part of overlap as a track back length. Under the restriction of the overlap length, this method is complicated in implementation and severe in performance degradation under high code rate, and the proportion of effective decoding time of each sub-block is low.